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\title{Opponent Modelling for a Mixed-Strategy Game between Police and Drivers}
\numberofauthors{3}
\author
{
	\alignauthor
		Julio Godoy\\
		\affaddr{University of Minnesota}\\
		\email{godoy@cs.umn.edu} 
	\alignauthor 
		Ernesto Nunes \\
		\affaddr{University of Minnesota}\\
		\email{enunes@cs.umn.edu}
	\alignauthor
		Grady Jensen\\
		\affaddr{University of Minnesota}\\
		\email{jensen@cs.umn.edu}
}

\CopyrightYear{2011}

\begin{document}
\maketitle

\begin{abstract}
This work introduces a multi-agent game in which there are police and driver
players and these agents play mixed-strategies. The goal of police agents is to catch speeding drivers, while the
goal of drivers is to get to their destinations quickly without incurring a ticket. We
model the game as an extensive-form game. We use two types of learners for each of the agent types:
reinforcement learners, and experience-weighted learners. The objectives of this work is twofold, first
to develop a graph-based model for modelling police and driver transient interactions. Second
to investigate the performance of each of the learning algorithms under different graph structures and
different number of agents.
\end{abstract}


\category{I.2.6}{Artificial Intelligence}{Learning}
\terms{}
\keywords{Game Theory, Q Learning, Experience Weighted Attraction, adversarial re-inforcement learning}

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